Developing more effective diagnostic tools for early detection and staging of lung cancer — both more accurate and less invasive — and ongoing research efforts are poised to usher in a paradigm shift in patient care. Advances in endoscopic techniques and in imaging help achieve these goals. Among endoscopy techniques are:

Early detection of endobronchial mucosal abnormalities by autofluorescence bronchoscopy and narrow-band imaging

Mayo Clinic is designing a novel imaging technique for staging subsolid lesions, known as CANARY. Through an integrated multidisciplinary approach, this is among the many innovative and minimally invasive strategies used at Mayo Clinic in the management of lung cancer.

Endobronchial ultrasound and staging

Accurate staging, and therefore management, of lung cancer requires access to the mediastinum for lymph node sampling. This has historically been accomplished by mediastinoscopy, a surgical procedure requiring a small skin incision and general anesthesia, but which gives only partial access to thoracic lymph node stations.

By contrast, the convex endobronchial ultrasound (EBUS) bronchoscope provides real-time guidance for transbronchial needle aspiration (TBNA) and allows access to more lymph node stations than does mediastinoscopy. EBUS is generally performed as an outpatient procedure under conscious sedation. It affords excellent sampling precision and minimal complications and has been routinely used at Mayo Clinic in Rochester, Minn., since 2006, with more than 400 procedures performed annually.

A growing body of evidence suggests that EBUS-TBNA has a diagnostic accuracy similar to mediastinoscopy. For lymph node stations not accessible via bronchoscopic EBUS, combining with transesophageal endoscopic ultrasound has proved complementary and allows complete mediastinal staging with excellent sensitivity and specificity. Importantly, EBUS-TBNA samples provide sufficient tissue for molecular analyses in the vast majority of cases, precluding the need for more invasive sampling procedures.

Early diagnosis of peripheral lung cancers

Many lung cancers present as peripheral lesions. For definitive diagnosis, transthoracic needle aspiration — or even surgical lung biopsy — is required. Both procedures are associated with a risk of morbidity, complications and occasional sampling error.

Mayo Clinic bronchoscopists now use electromagnetic navigation (EMN), which takes advantage of volumetric high-resolution computed tomography (HRCT) acquisition protocols to provide a virtual pathway to peripheral lesions. These pathways are fused with real-time bronchoscopy images, allowing access to lesions that are otherwise hidden. To do this, the physician advances a probe through the bronchoscope. Its location is determined relative to the targeted lesion by triangulation, which involves measuring the angles to it from known points as the patient lies within an electromagnetic field. A sheath is left in place within a bronchus, through which biopsy forceps or needles can be advanced to biopsy the lesion.

This revolutionary technology substantially increases the diagnostic yield of bronchoscopy and has minimal complications. Continuing refinements in the technology are promising and suggest that EMN will likely occupy an increasingly important role in the diagnosis of lung cancer.

Autofluorescence bronchoscopy and narrow-band imaging

Some cancers arise from the bronchial epithelium and are preceded by premalignant lesions not easily visualized by conventional bronchoscopy. Autofluorescence bronchoscopy (AB) and narrow-band imaging rely on certain patterns of absorption by abnormal tissues when exposed to light emitted at specified wavelengths. AB, also known as blue light bronchoscopy, can reveal abnormalities in tissue that may not be visible with white light.

These techniques may be utilized to detect pre-invasive endobronchial lesions amenable to early treatment which may prevent progression to invasive lung cancer. Furthermore, as the airway is not typically well characterized by computerized tomography (CT) imaging, these bronchoscopic techniques may prove complementary to CT screening for the early detection of lung cancer.

Novel visualization approach to speed diagnosis: CANARY

CANARY is a noninvasive quantitative imaging tool under development by Mayo Clinic scientists and physicians that uses robust, state-of-the-art machine learning and novel visualization techniques to model the building blocks of the lesion (color-coded density histograms based on volumetric analysis).

The use of imaging modalities such as CT continues to expand, regardless of lung cancer screening programs. As a consequence, clinicians are encountering an increasing number of incidentally detected peripheral lung subsolid lesions, for which management strategies have not been well established.

A key innovative, noninvasive technique that may help clarify the role of these lesions has been developed by a team of Mayo Clinic physicians and scientists led by Fabien Maldonado, M.D. Known as CANARY (computer-aided nodule assessment and risk yield), it is a novel technology that can help categorize lesions and may aid prognosis.

About ground-glass opacity (GGO)

Persistent GGO are usually classified as adenocarcinomas, ranging from premalignant lesions (atypical adenomatous hyperplasia) to invasive disease. They are generally slow-growing lesions. Because their significance can be difficult to determine, optimal follow-up and management are not clearly known. The prognosis of these lesions is driven by their histologic characteristics, which are available only after surgical resection.

To further validate this technology, CANARY researchers are using data from the National Lung Screening Trial (NLST) and partnering with the American College of Radiology Imaging Network. Early detection and categorization of GGOs by CANARY could ultimately result in significant advances in understanding and treating this subtype of lung cancer. The ability of CANARY to noninvasively identify tumors exhibiting aggressive behavior could avoid unnecessary delays in management and result in improved patient outcomes. Importantly, the ability to predict indolent behavior would help avoid unnecessary surgery.

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